AI agent · SQL · Internal tool · 2025–present
AI SQL Troubleshooter
An AI tool that reviews thousands of lines of SQL and proposes fixes in minutes.
AI workflow owner & reviewer
- Days to minutes
- turnaround on a broken query
- Human-gated
- designed for broader-team verification before release
Sanitized implementation diagram
Diagram · implementation viewExplainable SQL review with a human release gate
A confidentiality-safe representation of the internal review path.
Inspect
Accept a large, unfamiliar SQL codebase for review. Surface probable errors and the statements they affect.
Explain
Return a correction with plain-language reasoning.
Verify
Keep a human in the loop before a fix is shipped.
Sanitized workflow diagram. Published content excludes employer SQL, data, screenshots, and identifiers.
- My contribution
- Workflow design, prompt behavior, review standard, and iteration
- Hard constraint
- Employer SQL stays confidential and outside portfolio content
- What changed
- Explained corrections reduced reliance on a few specialists
Built for a business-intelligence team, this AI-assisted tool reads large SQL codebases, flags likely errors, and writes corrections with plain-language explanations. It lowers the expertise a fix requires from expert to confident, so the work stops piling up on a few people.
Outcomes
- Reviews thousands of lines of SQL and returns corrections in minutes.
- Designed to reduce reliance on a few specialists by pairing proposed fixes with explanations and a human release gate.
Evidence & claim boundaries
- Confidentiality
- No employer SQL, data, screenshots, or identifiers are published; the workflow is described at a safe abstraction level.
- Turnaround basis
- The comparison reflects the team’s troubleshooting workflow before and after the tool. Its evidence basis is an internal observation with variable response times; public benchmarking and a response-time service guarantee are outside this claim.
Problem
Debugging long, unfamiliar SQL is slow, and it lands on a handful of people who know the codebase. Everyone else waits on them, so every broken query became a queue behind two or three experts. Scarce access to people who could safely change the code created the bottleneck.
Approach
I wanted to widen the door while preserving the review standard. The tool had to do more than spot a problem. It had to explain the fix clearly enough that a non-expert could apply it and trust the result.
So I guided AI to create a reviewer that reads thousands of lines of SQL, points to likely errors, and proposes concrete corrections with the reasoning attached. Clear reasoning makes each correction reviewable and supports a confident release.
Delivered scope
A reviewer that ingests a large SQL codebase, surfaces probable errors, and returns specific corrections with an explanation. I direct refinements against cases that arise in practice, keeping the output grounded in real review work.
Key decisions & trade-offs
I required explanation alongside detection. The value is in returning a correction a non-expert can read, understand, and apply safely. I reviewed the output against that standard.
Technology LLM platforms · SQL
Scope Workflow design · AI guidance · Human review